Predicting cannulation difficulty in endoscopic retrograde cholangiopancreatography using CT image findings: a decision-tree analysis
- Lee, Sun Hwa; Lee, Jae Min; Han, Na Yeon; Kim, Min Ju; Park, Beom Jin; Sung, Deuk Jae; Sim, Ki Choon
- Issue Date
- SAGE PUBLICATIONS LTD
- Endoscopic retrograde cholangiopancreatography; multidetector computed tomography; decision-tree analysis; post-ERCP pancreatitis; precision medicine; image reconstruction
- Acta Radiologica, v.61, no.11, pp.1484 - 1493
- Journal Title
- Acta Radiologica
- Start Page
- End Page
Difficult cannulation during endoscopic retrograde cholangiopancreatography (ERCP) is associated with increased complications; therefore, its prediction is important.
To identify radiologic risk factors of difficult cannulation during ERCP based on computed tomography (CT) findings and to develop a predictive model for a difficult cannulation.
Material and Methods
A total of 171 patients with native papilla who underwent both enhanced CT and ERCP were recruited. Two radiologists independently measured the distal common bile duct (CBD) diameter and choledochoduodenal (CD) angle and analyzed CT images for presence of CBD stone and papilla bulging, size and type of periampullary diverticulum (PAD), and duodenal segment in which major papilla was located. Multivariate logistic regression analysis and decision-tree analysis were performed to identify risk factors for difficult cannulation.
Thirty-nine patients underwent a difficult cannulation. The multivariate logistic regression analysis revealed that a smaller CBD diameter, presence of papilla bulging, location of the major papilla other than the descending duodenum, a smaller CD angle, and a higher worrisome PAD score were statistically relevant factors for difficult cannulation (P < 0.049). In the decision-tree analysis, a higher worrisome PAD score was the strongest predictor of difficult cannulation, followed by the presence of papilla bulging, smaller CD angle, and a smaller CBD diameter. The predictive model had an 82.5% overall predictive accuracy.
The CT findings-based decision-tree analysis model showed a high accuracy in predicting cannulation difficulty and may be helpful for making pre-ERCP strategy.
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- 2. Clinical Science > Department of Gastroenterology and Hepatology > 1. Journal Articles
- 2. Clinical Science > Department of Radiology > 1. Journal Articles
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